Spatial-Time-Series data involves time-varying statistics associated with data sources called nodes having a spaced-apart relationship with one another. In communications networks, a node could be a switching office and the data could be the level of incoming and outgoing traffic for each five minute interval in a day. In other situations, a node could be a financial entity, (e.g., a bank) and the data could be the level of incoming and outgoing financial transactions for consecutive days. To analyze spatial-time-varying data, an analyst may employ a graph or map, in which symbols, for example, bars, are used to encode the level of the data at respective nodes for each time period. However, an analyst may be reluctant to use a map to analyze such data when the amount of data is large, which is usually the case when the number of nodes is large and/or the data is time-varying. The reason for such reluctance is that it would be an onerous task to interpret time-varying data.